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Abstract
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Article Information:
De-Noising and Segmentation of Brain MR images by Spatial Information and K-Means Clustering
Arshad Javed, Wang Yin Chai and Narayanan Kulathuramaiyer
Corresponding Author: Arshad Javed
Submitted: February 15, 2013
Accepted: March 14, 2013
Published: December 05, 2013 |
Abstract:
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Image Segmentation is the process of partitioning a digital image into non-overlapping distinct regions, so that significant information about the image could be retrieved and various analysis could be performed on that segmented image. The aim of this study is to reduce the noise, enhance the image quality by considering the spatial information without losing any important information about the images and perform the segmentation process in noise free environment. K-Means clustering technique is used for the purpose of segmentation of brain tissue classes which is considered more efficient and effective for the segmentation of an image. We tested the proposed technique on different types of brain MR images which generates good results and proved robust against noise. Conclusion had been concluded at the end of this study.
Key words: Cluster validity index, image segmentation, k-means, MRI, spatial information, ,
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Cite this Reference:
Arshad Javed, Wang Yin Chai and Narayanan Kulathuramaiyer, . De-Noising and Segmentation of Brain MR images by Spatial Information and K-Means Clustering. Research Journal of Applied Sciences, Engineering and Technology, (22): 4215-4220.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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